基于最优广义S变换和脉冲耦合神经网络的轴承故障诊断

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振动与冲击 ›› 2015, Vol. 34 ›› Issue (9) : 26-31.

论文

张云强，张培林，吴定海，李兵

作者信息 +

Bearing fault diagnosis based on optimal generalized S transform and pulse coupled neural network

ZHANG Yun-qiang, ZHANG Pei-lin, WU Ding-hai, LI Bing

Author information +

文章历史 +

摘要

针对滚动轴承故障信号具有明显的非线性和非平稳特征，提出一种基于最优广义S变换和脉冲耦合神经网络(PCNN)的故障特征提取方法。首先采用基于时频聚集性最优化的广义S变换获取轴承故障信号的时频表示，然后利用脉冲耦合神经网络对最优广义S变换时频图进行二值分解，提取二值图像的捕获比序列用于表达故障信号的故障特征。对滚动轴承4种状态信号进行分析，验证方法的有效性。结果表明该方法能够提取出更加有效的轴承故障特征参数，有利于提高轴承故障诊断的精度。

Abstract

For the nonlinear and non-stationary characteristics of rolling bearing fault signals, a feature extraction method based on optimal generalized S transform and pulse coupled neural network(PCNN) was proposed. Generalized S transform was optimized by measuring the time-frequency aggregation, and then utilized to achieve time-frequency representations of bearing fault signals. Time-frequency images were further decomposed into a series of binary images. The capture rate sequences of binary images were then defined and extracted as the bearing fault feature parameters. Rolling bearing signals of four different states were analyzed. The results indicate that the proposed method can extract more effective bearing fault feature parameters which are capable of improving the bearing fault diagnosis accuracy.

导出引用

张云强，张培林，吴定海，李兵. 基于最优广义S变换和脉冲耦合神经网络的轴承故障诊断[J]. 振动与冲击, 2015, 34(9): 26-31

ZHANG Yun-qiang, ZHANG Pei-lin, WU Ding-hai, LI Bing. Bearing fault diagnosis based on optimal generalized S transform and pulse coupled neural network[J]. Journal of Vibration and Shock, 2015, 34(9): 26-31

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